{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " # MLP example using PySNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import torch\n",
    "from torch.utils.data import DataLoader\n",
    "from tensorboardX import SummaryWriter\n",
    "from torchvision import transforms\n",
    "from tqdm import tqdm\n",
    "\n",
    "from pysnn.connection import Linear\n",
    "from pysnn.neuron import LIFNeuron, Input\n",
    "from pysnn.learning import MSTDPET\n",
    "from pysnn.encoding import PoissonEncoder\n",
    "from pysnn.network import SNNNetwork\n",
    "from pysnn.datasets import AND, BooleanNoise, Intensity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ## Parameter defintions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Architecture\n",
    "n_in = 10\n",
    "n_hidden = 5\n",
    "n_out = 1\n",
    "\n",
    "# Data\n",
    "duration = 200\n",
    "intensity = 50\n",
    "num_workers = 0\n",
    "batch_size = 1\n",
    "\n",
    "# Neuronal Dynamics\n",
    "thresh = 1.0\n",
    "v_rest = 0\n",
    "alpha_v = 10\n",
    "tau_v = 10\n",
    "alpha_t = 10\n",
    "tau_t = 10\n",
    "duration_refrac = 2\n",
    "dt = 1\n",
    "delay = 2\n",
    "i_dynamics = (dt, alpha_t, tau_t, \"exponential\")\n",
    "n_dynamics = (thresh, v_rest, alpha_v, alpha_v, dt, duration_refrac, tau_v, tau_t, \"exponential\")\n",
    "c_dynamics = (batch_size, dt, delay)\n",
    "\n",
    "# Learning\n",
    "epochs = 10\n",
    "lr = 0.1\n",
    "w_init = 0.8\n",
    "a = 0.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ## Network definition\n",
    " The API is mostly the same as for regular PyTorch. The main differences are that layers are composed of a `Neuron` and `Connection` type,\n",
    " and the layer has to be added to the network by calling the `add_layer` method. Lastly, all objects return both a\n",
    " spike (or activation potential) object and a trace object.\n",
    "\n",
    " NOTE: This network shows that PySNN is capable of defining cyclic graphs, since there is a feedback connection from neuron 2 to neuron 1, and recurrent connections on both layers. This network is by no means an example of a well performing network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Network(SNNNetwork):\n",
    "    r\"\"\"Example of a cyclic graph containing feedback and recurrent connections.\n",
    "    \n",
    "    This network is by no means an example of a well performing network.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self):\n",
    "        super(Network, self).__init__()\n",
    "\n",
    "        # Input\n",
    "        self.input = Input((batch_size, 1, n_in), *i_dynamics)\n",
    "\n",
    "        # Layer 1\n",
    "        self.mlp1_c = Linear(n_in, n_hidden, *c_dynamics)\n",
    "        self.mlp1_c.reset_weights(distribution=\"uniform\")  # initialize uniform between 0 and 1\n",
    "        self.neuron1 = LIFNeuron((batch_size, 1, n_hidden), *n_dynamics)\n",
    "        self.add_layer(\"fc1\", self.mlp1_c, self.neuron1)\n",
    "\n",
    "        # Recurrent connection for neuron1\n",
    "        self.mlp1_rec = Linear(n_hidden, n_hidden, *c_dynamics)\n",
    "        self.add_layer(\"fc1_rec\", self.mlp1_rec, self.neuron1)\n",
    "\n",
    "        # Layer 2\n",
    "        self.mlp2_c = Linear(n_hidden, n_out, *c_dynamics)\n",
    "        self.mlp2_c.reset_weights(distribution=\"uniform\")\n",
    "        self.neuron2 = LIFNeuron((batch_size, 1, n_out), *n_dynamics)\n",
    "        self.add_layer(\"fc2\", self.mlp2_c, self.neuron2)\n",
    "\n",
    "        # Recurrent connection for neuron 2\n",
    "        self.mlp2_rec = Linear(n_out, n_out, *c_dynamics)\n",
    "        self.add_layer(\"fc2_rec\", self.mlp2_rec, self.neuron2)\n",
    "\n",
    "        # Feedback connection from neuron 2 to neuron 1\n",
    "        self.mlp2_prev = Linear(n_out, n_hidden, *c_dynamics)\n",
    "        self.add_layer(\"fc2_back\", self.mlp2_prev, self.neuron1)\n",
    "\n",
    "    def forward(self, input):\n",
    "        x, t = self.input(input)\n",
    "\n",
    "        # Layer 1\n",
    "        x_prev, _ = self.mlp2_prev(self.neuron2.spikes, self.neuron2.trace)\n",
    "        x_rec, _ = self.mlp1_rec(self.neuron1.spikes, self.neuron1.trace)\n",
    "        x_forw, _ = self.mlp1_c(x, t)\n",
    "        x, t = self.neuron1([x_forw, x_rec, x_prev])\n",
    "\n",
    "        # Layer out\n",
    "        x_rec, _ =self.mlp2_rec(self.neuron2.spikes, self.neuron2.trace)\n",
    "        x_forw, _ = self.mlp2_c(x, t)\n",
    "        x, t = self.neuron2([x_rec, x_forw])\n",
    "\n",
    "        return x, t"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ## Dataset\n",
    " Simple Boolean AND dataset, generated to match the input dimensions of the network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_transform = transforms.Compose(\n",
    "    [\n",
    "        # BooleanNoise(0.2, 0.8),\n",
    "        Intensity(intensity)\n",
    "    ]\n",
    ")\n",
    "lbl_transform = transforms.Lambda(lambda x: x * intensity)\n",
    "\n",
    "train_dataset = AND(\n",
    "    data_encoder=PoissonEncoder(duration, dt),\n",
    "    data_transform=data_transform,\n",
    "    lbl_transform=lbl_transform,\n",
    "    repeats=n_in / 2,\n",
    ")\n",
    "train_dataloader = DataLoader(\n",
    "    train_dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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   "source": [
    "# Visualize input samples\n",
    "_, axes = plt.subplots(1, 4, sharey=True, figsize=(25, 8))\n",
    "for s in range(len(train_dataset)):\n",
    "    sample = train_dataset[s][0]  # Drop label\n",
    "    sample = sample.sum(-1).numpy()  # Total spike by summing over time dimension\n",
    "    sample = np.squeeze(sample)\n",
    "    axes[s].bar(range(len(sample)), sample)\n",
    "    axes[s].set_ylabel(\"Total number of spikes\")\n",
    "    axes[s].set_xlabel(\"Input neuron\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:09<00:00,  1.02it/s]\n"
     ]
    }
   ],
   "source": [
    "device = torch.device(\"cpu\")\n",
    "net = Network()\n",
    "\n",
    "# Learning rule definition\n",
    "layers = net.layer_state_dict()\n",
    "learning_rule = MSTDPET(layers, 1, 1, lr, np.exp(-1/10))\n",
    "\n",
    "# Training loop\n",
    "for _ in tqdm(range(epochs)):\n",
    "    for batch in train_dataloader:\n",
    "        sample, label = batch\n",
    "\n",
    "        # Iterate over input's time dimension\n",
    "        for idx in range(sample.shape[-1]):\n",
    "            input = sample[:, :, :, idx]\n",
    "            spike_net, _ = net(input)\n",
    "            \n",
    "            # Determine reward, provide reward of 1 for desired behaviour, 0 otherwise.\n",
    "            # For positive samples (simulating an AND gate) spike as often as possible, for negative samples spike as little as possible.\n",
    "            if spike_net.long().view(-1) == label:\n",
    "                reward = 1\n",
    "            else:\n",
    "                reward = 0\n",
    "\n",
    "            # Perform a single step of the learning rule\n",
    "            learning_rule.step(reward)\n",
    "\n",
    "        # Reset network state (e.g. voltage, trace, spikes)\n",
    "        net.reset_state()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Write graph to tensorboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "writer = SummaryWriter()\n",
    "writer.add_graph(net, sample[...,0])\n",
    "\n",
    "writer.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Screenshot of graph in TensorBoard"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](cyclic_recurrent_graph.png \"Cyclic and recurrent graph\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate Data for Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "out_spikes = []\n",
    "out_voltage = []\n",
    "out_trace = []\n",
    "\n",
    "for batch in train_dataloader:\n",
    "    single_out_s = []\n",
    "    single_out_v = []\n",
    "    single_out_t = []\n",
    "    sample, _ = batch\n",
    "\n",
    "    # Iterate over input's time dimension\n",
    "    for idx in range(sample.shape[-1]):\n",
    "        input = sample[:, :, :, idx]\n",
    "        spike_net, trace_net = net(input)\n",
    "\n",
    "        # Single timestep results logging\n",
    "        single_out_s.append(spike_net.clone())\n",
    "        single_out_t.append(trace_net.clone())\n",
    "        single_out_v.append(net.neuron2.v_cell.clone())  # Clone the voltage to make a copy of the value instead of using a pointer to memory\n",
    "\n",
    "    # Store batch results\n",
    "    out_spikes.append(torch.stack(single_out_s, dim=-1).view(-1))\n",
    "    out_voltage.append(torch.stack(single_out_v, dim=-1).view(-1))\n",
    "    out_trace.append(torch.stack(single_out_t, dim=-1).view(-1))\n",
    "\n",
    "    # Reset network state (e.g. voltage, trace, spikes)\n",
    "    net.reset_state()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize output neuron state over time\n",
    "\n",
    "In the voltage plots the peaks never reach the voltage of 1, this is because the network has already reset the voltage of the spiking neurons during the forward pass. Thus it is not possible to register the exact voltage surpassing the threshold.\n",
    "\n",
    "Note that the output is pushing towards maximum firing rate due to the reciprocal connection in the network. These results do not mean anything."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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    "_, axes = plt.subplots(3, 4, sharey=\"row\", figsize=(25, 12))\n",
    "\n",
    "# Process every sample separately\n",
    "for s in range(len(out_spikes)):\n",
    "    ax_col = axes[:, s]\n",
    "    spikes = out_spikes[s]\n",
    "    voltage = out_voltage[s]\n",
    "    trace = out_trace[s]\n",
    "    data_combined = [spikes, trace, voltage]\n",
    "    names = [\"Spikes\", \"Trace\", \"Voltage\"]\n",
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    "    # Set column titles\n",
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